Statistical Structural Health Monitoring and Damage Detection for Highly Variable Environments

University Of Utah, Salt Lake City UT

Investigators

Abstract

Structural health monitoring aims to detect damage and assess critical, physical infrastructures over long periods of time. The infrastructures assessed can include pipelines, bridges, steel cables, aircrafts, trains, and power plants. Due to the significant complexity and variability of structural health monitoring data, current technology can only reasonably assess limited amounts of information. Yet, as structural health monitoring expands to more complex problems and its data includes hundreds of terabytes of structural data over several decades, big data strategies become essential. This award supports fundamental research to integrate statistical, big data strategies with structural health monitoring systems. The new, integrated framework will enable the tracking and the anticipation of structural damage in harsh, highly variable environments over many years. This study will provide tools for the detection of structural damage before catastrophic failures occur. Results from this research will benefit the U.S. economy and society by improving safety and reducing life-cycle costs of many civil and mechanical systems, from airplanes to bridges to pipelines. The research also promotes highly interdisciplinary collaboration as the research employs methods and solves problems found across the disciplines of electrical and computer engineering, computer science, mechanical engineering, and civil engineering. This diverse subject matter will promote a multidisciplinary engineering education and help to recruit and broaden the participation of underrepresented students in research. This project will statistically identify barely visible, critically important trends in large (gigabyte to terabyte size) structural health monitoring data sets. Three primary challenges are addressed: (1) distorting environmental conditions, (2) poor scaling to large data sets, and (3) no standard statistic for reliability. This project addresses these three challenges through the creation of a modular big data structural health monitoring framework based on dynamic time warping, singular value decomposition, factor analysis, and maximum likelihood statistics. The big data structural health monitoring framework is assessed through several short-term (hours to days) and long-term (multi-year) structural health monitoring experiments that introduce composite panels to a variety of damage and environmental conditions. These experiments provide statistical validation as well as a unique opportunity to study the effects of environmental parameters on composite structural health monitoring data.